Abstract
We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.
Highlights
Hundreds of thousands of individuals with rare disorders are undergoing whole-genome sequencing in an effort to identify novel disease etiologies, increase our understanding of biological processes, and improve clinical care.[1]
The statistical association methods required to identify relevant loci need to fulfil several criteria in order to be well-powered, when the number of cases with a particular disease is small. They need to allow some sharing of information across variants because rare diseases are often genetically heterogeneous. They need to account for the presence of pathogenic rare variants that act upon disease risk in a dominant or a recessive manner alongside benign rare variants that do not affect disease risk
We reviewed various methods for estimating the evidence of a model[9] and chose the method of power posteriors,[10] which enables the evidence to be estimated by Markov chain Monte Carlo (MCMC) sampling
Summary
Hundreds of thousands of individuals with rare disorders are undergoing whole-genome sequencing in an effort to identify novel disease etiologies, increase our understanding of biological processes, and improve clinical care.[1]. The statistical association methods required to identify relevant loci need to fulfil several criteria in order to be well-powered, when the number of cases with a particular disease is small They need to allow some sharing of information across variants because rare diseases are often genetically heterogeneous. They need to account for the presence of pathogenic rare variants that act upon disease risk in a dominant or a recessive manner alongside benign rare variants that do not affect disease risk They must be capable of integrating prior information into the inference regarding the plausibility of a locus being implicated in a disease and variant-level co-data on pathogenicity. Methods need to have efficient implementations that enable fast application across a large number of regions in the genome
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